دورية أكاديمية
Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy
| العنوان: | Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy |
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| المؤلفون: | Ricky Hu, Ishita Chen, Jacob Peoples, Jean-Paul Salameh, Mithat Gönen, Paul B. Romesser, Amber L. Simpson, Marsha Reyngold |
| المصدر: | Physics and Imaging in Radiation Oncology, Vol 24, Iss , Pp 36-42 (2022) |
| بيانات النشر: | Elsevier, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Medical physics. Medical radiology. Nuclear medicine LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| مصطلحات موضوعية: | Radiomics, Artificial intelligence, Machine learning, Computer vision, Survival analysis, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| الوصف: | Background and Purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of metastatic environments. To investigate the feasibility of analyzing CT texture, we sought to build an automated model to predict progression-free survival using CT radiomics and artificial intelligence (AI). Materials and Methods: Liver CT scans and outcomes for N = 97 CLM patients treated with radiotherapy were retrospectively obtained. A survival model was built by extracting 108 radiomic features from liver and tumor CT volumes for a random survival forest (RSF) to predict local progression. Accuracies were measured by concordance indices (C-index) and integrated Brier scores (IBS) with 4-fold cross-validation. This was repeated with different liver segmentations and radiotherapy clinical variables as inputs to the RSF. Predictive features were identified by perturbation importances. Results: The AI radiomics model achieved a C-index of 0.68 (CI: 0.62–0.74) and IBS below 0.25 and the most predictive radiomic feature was gray tone difference matrix strength (importance: 1.90 CI: 0.93–2.86) and most predictive treatment feature was maximum dose (importance: 3.83, CI: 1.05–6.62). The clinical data only model achieved a similar C-index of 0.62 (CI: 0.56–0.69), suggesting that predictive signals exist in radiomics and clinical data. Conclusions: The AI model achieved good prediction accuracy for progression-free survival of CLM, providing support that radiomics or clinical data combined with machine learning may aid prognostic assessment and management. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2405-6316 |
| العلاقة: | http://www.sciencedirect.com/science/article/pii/S240563162200080XTest; https://doaj.org/toc/2405-6316Test |
| DOI: | 10.1016/j.phro.2022.09.004 |
| الوصول الحر: | https://doaj.org/article/b2ec384fe5594768a559e9b8b7e2d495Test |
| رقم الانضمام: | edsdoj.b2ec384fe5594768a559e9b8b7e2d495 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 24056316 |
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| DOI: | 10.1016/j.phro.2022.09.004 |